This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. The subproject and investigator (PI) may have received primary funding from another NIH source, and thus could be represented in other CRISP entries. The institution listed is for the Center, which is not necessarily the institution for the investigator. The development of high dimensional brain mapping in the field of computational anatomy (CA) and its integration with other state-of-the-art brain imaging technologies continues to be a unique opportunity to study both the underlying neurobiology of brain structure and its connections, and the relationships between brain structure abnormalities and patterns of cognitive deficits. Such mapping tools permit the precise formulation of hypotheses concerning brain structure and function determined by patterns of connectivity and shape particularly in development and neurodegeneration. Over the past fifteen years, many investigators have been studying the shape and structure of the human brain in multiple anatomies in common coordinates (e.g.8, 33, 39, 41, 42, 61, 63, 65, 80, 88, 90, 101, 136, 151, 152). Further, emerging methodologies that integrate anatomical and functional information from multiple data provide an opportunity to ask detailed anatomical questions in a single set of standard coordinates. It is now possible to perform functional measurements and anatomical measurements at roughly 1.0 mm resolution. Systems now exist in isolation of each other for examining gray matter reconstructions of the neocortex, studying the gyrification, folding and sulcal patterning of the gray/white boundary of the neocortex, as well as the anatomical size and shape of deep nuclei in the brain such as the hippocampus, thalamus and caudate. Our own group has been involved in the development of these tools, including surface and volume mapping tools, cortical and surface generation tools, gyral and sulcal curve generation and the analysis of these structural data. Designed and developed in the 1st grant period, these methods are now being used by investigators around the world in structural studies of the neocortex and deep nuclei in a variety of neurodevelopmental and neurodegenerative processes. Recent developments in observing the activation of brain regions via functional magnetic resonance imagery (fMRI) while different tasks are being processed are now providing a clear look at the working of this marvellous machinery. Such studies are expected to reveal an in depth understanding of the intricate and effortless processing humans can perform while they go about in their daily lives. Knowledge gained from such studies is expected to provide better understanding of the normal mechanisms and aberrations to these mechanisms in developmental situations. Furthermore, diffusion tensor magnetic resonance imaging (DT-MRI or DTI) provides useful physiological information noninvasively, not only about the fiber structure of normal tissue, but also about its changes in development, disease and degeneration. It has already been shown to be of value in studies of neuroanatomy, fiber connectivity, and brain development. DT-MRI has been used in the investigation of cerebral ischemia, brain maturation and traumatic brain injury. It also promises to further our understanding of brain disorders and abnormalities such as stroke, tumors and metabolic disorders, epilepsy, multiple sclerosis, schizophrenia, Alzheimer's disease and cognitive impairment. Our collaborators have now begun to use fMRI and DT-MRI data to understand the intricate functional properties of the whole brain as well as the neocortex. To this end, the aims of TRD4 are to extend previously developed CA tools for the neocortex to the whole brain and develop additional CA tools for analyzing functional and connectivity data in brain and cortical structures. The tools will be made available to the wider scientific community. In particular, the proposed developments should benefit a wide range of clinical disciplines from psychiatry to pediatrics including those funded by the following NIH grants: 1. developmental disability (Denckla, Mostofsky, Cutting, Scarborough, Naidu) - perform functional and longitudinal analysis of cognitive processes on frontal lobe, basal ganglia and subcortical regions associated with developmental disability (Aims 1, 4, 5) 2. attention and memory (Yantis, Stark and Courtney-Faruqee) [unreadable]perform structural and functional analysis of cognitive processes on hippocampus and associated cortical surfaces implicated in attention and memory (Aims 1, 4, 5) 3. neurodegeneration/dementia (Albert, Csernansky, Reading) - perform structural and functional analysis of stages of neurodegeneration associated with the hippocampus, caudate, cingulate, parahippocampal gyrus, prefrontal cortex as well as gyral and sulcal folds of these structures (Aims 1, 4, 5), and co-register neuronal connections (Aims 2,3) 4. pediatric ischemia/trauma (Graham, Hoon, Christensen, Levin) - perform structural and functional analysis of stages of neurodegeneration associated with white matter tracts and structures (Aims 1, 4, 5), and co-register neuronal connections (Aims 2,3) during growth 5. Stroke (Hillis) - perform structural and functional analysis of developmental stages of neuronal diseases (Aims 1, 4, 5), and co-register neuronal connections (Aims 2, 3) 6. pediatric brain tumors (Horska) - perform structural and functional analysis of stages of neurodegeneration associated with white matter tracts and structures (Aims 1, 4, 5), and coregister neuronal connections (Aims 2,3) during growth 7. depression and mood disorders (Botteron, Pearlson) [unreadable]perform structural and functional analysis of developmental stages of diseases associated with the prefrontal cortex and hippocampus as well as gyral and sulcal folds of these structures (Aims 1, 4, 5);co-register neuronal connections between substructures (Aims 3, 4). 8. biomedical informatics (Rosen) [unreadable]perform large scale multi-site shape analysis of brain structures and substructures (Aims 1, 4, 5) and of neuronal connections (Aims 2, 3) Our specific aims are to integrate such structural and functional analysis tools into a software system by developing the following algorithms: Aim 1: Large Deformation Diffeomorphic Metric Mapping (LDDMM) for landmarks, curves, surfaces and volumes in whole brain analysis and registration; Aim 2: LDDMM for Diffusion Tensor images (LDDMM-DT) and tensor algebra; Aim 3: LDDMM for longitudinal and developmental analysis; Aim 4: LDDMM and signal processing methods for Functional Anatomy Building a software system that supports the data structures of curves, surfaces, and scalar and tensorial lattices of volumes will be essential in utilizing methods developed in TRD1 and TRD3 in studying brain structure and function.